Publication: Development Of Process Monitoring Solutions For Integrating Legacy Machine Tools to Industry 4.0 Framework
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Date
2025-01-15
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Indian Institute of Technology, Jodhpur
Abstract
Industry 4.0 envisages the implementation of sensors, Internet of Things (IoT)-based automation, and communication elements on shop floors to connect machine tools with networked systems and achieve "smart" functionalities. For example, product quality and productivity improvements can be accomplished effectively through real-time data acquisition and monitoring of manufacturing processes. The existing installation of legacy machines is a significant obstacle in realizing the potential benefits of smartness, as it offers no or limited adaptability to these changes. The implementation is further complicated in Small and Medium-sized Enterprises (SMEs) due to diverse manufacturing capabilities, restricted financial resources, and a scarcity of skilled human resources. Therefore, it is imperative to develop scalable and cost-effective solutions that enable SMEs to achieve process monitoring functions without significant investments. The doctoral thesis addresses these challenges by developing a digital assistant and platform-based manufacturing process monitoring architecture to integrate the legacy machine tools in the Industry 4.0 paradigm. The effectiveness of process monitoring solutions developed using these elements is corroborated by conducting a pilot study on the legacy engine lathe, drilling, and milling machines. The digital assistant acts as a decision support system to improve perceptions of legacy machine tool operators with broader skill sets in identifying process faults and monitoring component dimensions. Traditionally, machine operators monitor the process by frequently stopping the operation to verify specific characteristics, reducing productivity. The digital assistant captures the expertise of skilled operators to provide continuous monitoring and guidance during the operation. It utilizes a sensor-based system, feature extraction techniques, and a novel approach motivated by learning through demonstration for digitizing the expertise. The pilot study developed a digital assistant for component dimensions, tool wear state, and chatter onset monitoring for sample machine tools. The results demonstrated the effectiveness of a digital assistant in identifying in-process component dimensions, capturing tool wear states, and detecting chatter onset. A platform-based system, Manufacturing Process Monitoring as a Service (MPMaaS), was conceptualized to address challenges with the inaccessibility of human resources, which have technical expertise in developing and implementing process monitoring solutions. The architecture contains three layers: the user layer, the expert layer, and the hardware and software layer. It enables efficient interactions for requirement assessment and development of process monitoring solutions using technical experts of varied skill sets without ownership of hardware, software, and human resources. The pilot study on implementing MPMaaS was conducted to demonstrate experts' responsibilities and user-expert interactions while realizing a process monitoring solution. Industry 4.0 enables access to value-added services by integrating process monitoring outcomes with the enterprise network. However, a unified protocol is required to enable machine-machine, human-machine, and machine-system communications. Digital Manufacturing Infrastructure (DMI) is proposed in the thesis to integrate the digital assistant and MPMaaS at the machine, enterprise, and service-provider levels. The unified information model is developed using the Open Platform Communications Unified Architecture (OPC-UA) protocol at the machine level to transmit the data at the enterprise level for additional services such as operator performance monitoring, machine utilization, process control, and quality management through a centralized dashboard. The service provider level enables data and resource sharing for business functions at the enterprise level. A pilot study demonstrates the integration of legacy machines as connected entities in realizing digital SMEs. The thesis outcomes present various monitoring solutions facilitating the integration of vast legacy machines into the Industry 4.0 paradigm.
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Dayam, Sunidhi(2018).Development Of Process Monitoring Solutions For Integrating Legacy Machine Tools to Industry 4.0 Framework (Doctor's thesis).Indian Institute of Technology, Jodhpur